Marine macroalgae (commonly known as seaweeds), a rich yet underexplored resource, have emerged as a promising source of bioactive compounds with potent antioxidant properties. While oxidative stress is a critical factor in the pathogenesis of numerous chronic diseases, including neurodegenerative disorders, cardiovascular conditions, and cancer, macroalgae-derived compounds such as polyphenols, carotenoids, peptides, and sulfated polysaccharides have demonstrated the ability to mitigate oxidative damage through multifaceted mechanisms. These compounds neutralize reactive oxygen species and modulate key cellular pathways involved in inflammation and apoptosis. Despite significant advancements, gaps persist in understanding the pharmacokinetics, bioavailability, and clinical applications of these bioactives. Additionally, the inefficiencies of traditional extraction methods call for adopting innovative, environmentally friendly techniques that preserve bioactivity. This review synthesizes current knowledge on the therapeutic potential of macroalgal bioactives, acknowledges the contributions of other marine algae where relevant, highlights challenges in their extraction, and proposes future directions for research and application.

There is currently great interest in photocatalytic degradation technologies of pollutants in industrial effluents. This is due to the need to reduce the environmental pollution generated by the textile industry’s high demand of clothing for fast fashion; in addition to severe environmental problems, this also generates social problems. Since the catalysts of this type of processes are usually nanoparticles of metal oxides such as zinc and titanium, it is necessary to promote research into the synthesis and evaluation of photocatalysts. Therefore, this article describes three free basic access tools for the academic analysis of nanoparticles, from experimental design to representation, using the study of kinetics and particle size analysis. After pre-selecting easily accessible software, it was found that RStudio, J-Image, and Vesta are very useful programs for the analysis of nanoparticles in the respective areas of statistical processing, image analysis, and three-dimensional representation.

Urban Heat Islands (UHI) are a climatic phenomenon where urban areas exhibit significantly higher temperatures than their rural surroundings. This effect is primarily driven by urbanization, the reduction of green spaces, the prevalence of heat-retaining materials such as concrete and asphalt, and human activities. This study analyzes the UHI effect in Los Mochis, Sinaloa, México through temperature monitoring at three meteorological stations across different periods of the year. Spatial data processing and analysis were conducted using ArcMap 10.2, enabling the creation of thermal distribution maps and the identification of temperature variation patterns within the urban landscape. The results confirm the presence of the UHI effect, with urbanized zones recording consistently higher temperatures than less developed areas. The highest temperature, 39.53°C, was observed between July and September, whereas the maximum recorded during January-February was 28.62°C. Temperature variations also fluctuated depending on the time of day, with the highest value occurring at midday and in the afternoon. The study highlights the necessity of implementing mitigation strategies to counteract UHI effects, such as increasing green areas, incorporating reflective materials in infrastructure, and promoting sustainable urban design. These measures are essential for enhancing urban climate resilience and improving the quality of life in affected areas.

This research communication presents an analysis of dynamic supply chains (DSCs). The main goal of model-based cybertronics is to approximate, via a mathematical model from a dynamical system, the dynamics and behavior of dynamic supply chains. This considers that is at the operational level, where automation and control theory approaches take an insight —in this case, via Lyapunov stability—as a way to extend the use of mechatronic systems. Three case studies are presented: Firstly, the mathematical modeling and stability analysis of the ball-and-beam problem, as an approximation of a two echelon supply chain. Secondly, the mathematical modeling and stability analysis of a cold chain with temperature monitoring, and its relationship to inventory levels, are presented. From a theoretical perspective, applying model-based cybertronics in DSCs has direct practical implications: it improves operational control, enhances decision-making, and optimizes inventory management, particularly in cold chains. By treating high-volume supply chains as dynamical systems, managers can anticipate fluctuations and quantify efficiency. Finally, Lyapunov stability analysis ensures that models reliably reflect real-world behavior, enabling automation and predictable performance at an operational level in DSCs.

This technical note aims to present a theoretical analysis for cybertronics engineering (CE) for a class of dynamic supply chains for artificial intelligence (AI)-based products, services or hybrid solutions. The cybertronics-based analysis encompasses three classes of supply chains: (1) energy-based dynamic supply chains (DSC); (2) semiconductor manufacturing and quantum supply chains; and (3) retailing of the AI-based DSC solutions generated. Considering the nonlinear nature of DSC, to provide solutions for products and services based on AI-chained supply chains, novel robust control is addressed via sliding mode control (SMC) with proper stability analysis for the DSC.